Paper Info
Title
Maintainability prediction of object-oriented software system by multilayer perceptron model
Abstract
To accomplish software quality, correct estimation of maintainability is essential. However there is a complex and non-linear relationship between object-oriented metrics and maintainability. Thus maintainability of object-oriented software can be predicted by applying sophisticated modeling techniques like artificial neural network. Multilayer Perceptron neural network is chosen for the present study because of its robustness and adaptability. This paper presents the prediction of maintainability by using a Multilayer Perceptron (MLP) model and compares the results of this investigation with other models described earlier. It is found that efficacy of MLP model is much better than both Ward and GRNN network models.
Year
DOI
Venue
2012
10.1145/2347696.2347703
ACM SIGSOFT Software Engineering Notes
Keywords
Field
DocType
object-oriented software system,grnn network model,multilayer perceptron model,maintainability prediction,object-oriented metrics,multilayer perceptron,mlp model,non-linear relationship,correct estimation,artificial neural network,multilayer perceptron neural network,software quality,object-oriented software,principal component analysis,neural network,maintainability,prediction
Data mining,Computer science,Software system,Robustness (computer science),Software,Multilayer perceptron,Artificial intelligence,Artificial neural network,Software quality,Machine learning,Maintainability,Network model
Journal
Volume
Issue
Citations 
37
5
10
PageRank 
References 
Authors
0.50
11
3
Name
Order
Citations
PageRank
Sanjay Kumar Dubey1384.72
Ajay Rana2221.05
Yajnaseni Dash3182.11